大数据生态系统修仙之道 Hadoop Blog

2019-01-22 Docs Language:简体中文 & English Programing Language:Hadoop Website:www.geekparkhub.com GeekDeveloper:JEEP-711 Github:github.com/geekparkhub Gitee:gitee.com/geekparkhub

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大数据生态系统修仙之道 Hadoop Blog

1. 大数据简介

大数据是指无法在一定时间内用常规软件工具对其内容进行抓取、管理和处理的数据集合。大数据技术,是指从各种各样类型的数据中,快速获得有价值信息的能力。适用于大数据的技术,包括大规模并行处理(MPP)据库,数据挖掘电网,分布式文件系统,分布式数据库,云计算平台,互联网,和可扩展的存储系统,大数据由巨型数据集组成,这些数据集大小常超出人类在可接受时间下的收集、庋用、管理和处理能力,大数据的大小经常改变,截至2012年,单一数据集的大小从数太字节(TB)十兆亿字节(PB)等. —— MBA智库百科

2. 大数据概论

大数据概念

大数据(BigData)是指无法在一定时间范围内用常规软件工具进行捕捉、管理和处理的数据集合,是需要新处理模式才能具有更强的决策力、洞察发现力和流程优化能力的海量、高增长率和多样化的信息资产.

大数据主要解决:海量数据的存储和海量数据的分析计算问题.

数据存储单位: bit / Byte / KB / MB / GB / TB / PB / EB / ZB / YB / BB / NB / DB

1 Byte = 8bit
1 KB = 1024Byte
1 MB = 1024KB
1 GB = 1024MB
1 TB = 1024GB
1 PB = 1024TB
1 EB = 1024PB
1 ZB = 1024EB
1 YB = 1024ZB
1 BB = 1024YB
1 NB = 1024BB
1 DB = 1024NB

大数据特点(4V)

1.Volume (大量)

截止目前,人类生产的所有印刷材料的数据量是200PB(1PB=1024TB),而历史上全人类总共说过的话的数量大约是5EB(1EB=1024PB),当前典型个人计算机硬盘的容量为TB量级,而一些大企业的数据量已经近EB量级.

2.Velocity (高速)

这是大数据区分于传统数据挖掘的最显著特征,根据IDC的”数字宇宙”报告,预计2020年,全球数据使用量将达到35.2ZB(1 ZB = 1024EB),在如此海量的数据面前,处理数据的效率就是企业的生命.

3.Variety (多样)

这种类型的多样性也让数据被分为结构化数据和非结构化数据,相对于以往便于存储的以数据库&文本为主的结构化数据,非结构化数据越来越多,包括网络日志,音频,视频,图片,地理位置信息等,这些多类型的数据对数据的处理能力提出了更高要求.

4.Value (低价值密度)

价值密度的高低与数据总量的大小成反比,如何快速对有价值数据”提纯”,成为目前大数据背景下待解决的难题.

大数据应用场景

物流仓储:大数据分析系统助力商家精细化运营,提升销量,节约成本.

零售:分析用户消费习惯,为用户购买商品提供方便,从而提升商品售量,经典故事案例 -《纸尿布+啤酒》.

旅游:深度结合大数据能力与旅游行业需求,共建旅游产业智慧管理,智慧服务,智慧营销的未来.

商品广告推荐:为用户推荐可能喜欢的商品.

保险:海量数据挖掘及风险预测,助力保险行业精准营销,提升精细化定价能力.

金融:多维度体现用户特征,帮助金融机构推荐优质客户,防范欺诈风险.

房产:大数据全面助力房地产行业,打造精准投策与营销,挑选出更合适的地域.

大数据部门业务流程分析

大数据部门组织结构(重点)

大数据部门组织结构

所在组	所在组工作职责
平台组	Hadoop,Flume,Kafka,Hbase,Spark等框架平台搭建,集群性能监控,集群性能调优
数据仓库组	ETL工程师-数据清洗,Hive工程师-数据分析,数据仓库建模
数据挖掘组	算法工程师推荐系统工程师用户画像工程师
数据报表开发组	JAVAEE工程师

3. 探讨Hadoop框架大数据生态

Hadoop 简介

Apache Hadoop是一款支持数据密集型分布式应用程序并以Apache 2.0许可协议发布的开源软件框架,它支持在商品硬件构建的大型集群上运行的应用程序,Hadoop是根据谷歌公司发表的MapReduce和Google文件系统的论文自行实现而成。所有的Hadoop模块都有一个基本假设,即硬件故障是常见情况,应该由框架自动处理。

Hadoop框架透明地为应用提供可靠性和数据移动,它实现了名为MapReduce的编程范式:应用程序被分割成许多小部分,而每个部分都能在集群中的任意节点上运行或重新运行。此外,Hadoop还提供了分布式文件系统,用以存储所有计算节点的数据,这为整个集群带来了非常高的带宽。MapReduce和分布式文件系统的设计,使得整个框架能够自动处理节点故障,它使应用程序与成千上万的独立计算的计算机和PB级的数据连接起来,现在普遍认为整个Apache Hadoop”平台”包括Hadoop内核、MapReduce、Hadoop分布式文件系统(HDFS)以及一些相关项目,有Apache Hive和Apache HBase等等. —— 维基百科

Hadoop 是什么

Hadoop是由Apache基金会所开发的分布式系统基础架构.

Hadoop主要解决:海量数据的存储和海量数据的分析计算问题

广义上来讲,Hadoop通常是指一个更广泛的概念 — Hadoop生态圈

Hadoop 发展历史

1.Hadoop创始人:Doug Cutting

Lucene框架是Doug Cutting开创的开源软件,使用java编程语言开发,实现与Google类似的全文搜索功能,它提供了全文检索引擎的架构,包括完整的查询引擎和索引引擎.

2.2001年年底Lucene成为Apache基金会的一个子项目.

3.对于海量数据的场景,Lucene面对与Google同样的困难,存储数据困难,检索速度慢.

4.学习和模仿Google解决这些问题的办法:(Lucene的升级版) Nutch.

5.可以说Google是Hadoop的思想之源(Google在大数据方面的三篇论文)

GFS —> HDFS
Map-MapReduce —> MR
BigTable —> Hbase

6.2003至2004年,Google公开了部分GFS和MapReduce思想细节,以此为基础Doug Cutting等开发者用了2年业余时间实现了DFS和MapReduce机制,使Nutch性能飙升.

7.2005年Hadoop作为Lucene的子项目,Nutch的一部分正式引入Apache基金会.

8.2006年3月份,Map-Reduce和NDFS(Nutch Distributed File System),分别被纳入称为Hadoop的项目中.

9.Hadoop名字来源于Doug Cutting孩子的玩具大象.

10.Hadoop就此诞生并迅速发展,标志着大数据时代来临.

Hadoop 三大发行版本

Hadoop 三大发行版本 Apache | Cloudera | Hortonworks

Apache Hadoop

Apache版本最最原始(最基础)版本,对于入门学习最佳.

官网地址 : http://hadoop.apache.org/releases.html

下载地址 : https://archive.apache.org/dist/hadoop/common/

Cloudera Hadoop

Cloudera在大型互联网企业中应用场景较多.

官网地址 : https://www.cloudera.com/downloads/cdh/5-10-0.html

下载地址 : http://archive.cloudera.com/cdh5/cdh/5/

2008年成立的Cloudera是最早将Hadoop商用公司,为合作伙伴提供Hadoop的商业解决方案,主要是包括支持,咨询服务,培训.

2009年Hadoop创始人Doug Cutting也加盟了Cloudera公司,Cloudera产品主要为CDH,Cloudera Manager | Cloudera Support.

CDH是Cloudera的Hadoop发行版,完全开源,比Apache Hadoop在兼容性,安全性,稳定性上有所增强.

Cloudera Manager是集群的软件分发及管理监控平台,可以再几个小时内部部署好一个Hadoop集群,并对集群节点及服务
进行实时监控,Cloudera Support即是对Hadoop的技术支持.

Cloudera的标价为每年每个节点4000美元,Cloudera开发并贡献了可实时处理大数据的Impala项目.

Hortonworks Hadoop

Hortonworks文档较好.

官网地址 : https://hortonworks.com/products/data-center/hdp/

下载地址 : https://hortonworks.com/downloads/#data-platform

2011年成立的Hortonworks是雅虎与硅谷风投公司Benchmark Capital合资组建.

公司成立之初就吸纳了大约25名至30名专门研究Hadoop的雅虎工程师,上述工程师均在2005年开始协助雅虎开发Hadoop,并贡献了80%的Hadoop代码.

雅虎工程副总裁,雅虎Hadoop开发团队负责人Eric Baldeschwieler出任Hortonworks的首席执行官.

Hortonworks主打产品是Hortonworks Data Platform(HDP),也同样是100%开源产品,HDP除常见的项目外还包括Ambari,一款开源的安装和管理系统.

HCatalog 一个元数据管理系统,HCatalog现已集成到Facebook开源的Hive中,Hortonworks的Stinger开创性的极大的优化了Hive项目,Hortonworks为入门提供一个非常好的易于使用的沙盒.

Hortonworks开发了很多增强特性并提交至核心主干,这使得Apache Hadoop能够在包括Window Server和Windowns Azure在内的Microsoft Windows平台上本地运行,定价以集群为基础,每10个节点每年为12500美元.

Hadoop 优势 (4高)

1.高可靠性

Hadoop底层维护多个数据副本,所以即使Haoop某个计算元素或存储出现故障,也不会导致数据的丢失.

2.高扩展性

在集群间分配任务数据,可方便的扩展数以千计的节点.

3.高效性

在MapReduce的思想下,Hadoop是并行工作,以加快任务处理速度.

4.高容错性

能够自动将失败的任务重新分配.

Hadoop 组成(面试重点)

Hadoop1.x与Hadoop2.x 区别

Hadoop1.x组成 : MapReduce(计算+资源调度) | HDFS(数据存储) | Common(辅助工具)

Hadoop2.x组成 : MapReduce(计算) | Yarn(资源调度) | HDFS(数据存储) | Common(辅助工具)

在Hadoop1.x时代,Hadoop中的MapReduce同时处理业务逻辑运算和资源的调度,所以耦合性较大.

在Hadoop2.x时代,增加了Yarn,Yarn只负责资源的调度,MapReduce只负责运算.

HDFS 架构概述

HDFS (Hadoop Distributed File System) | 三大组件 nn / dn / 2nn

1.NameNode(nn) : 存储文件的元数据,如文件名,文件目录结构,文件属性(生成时间,副本数,文件权限,),以及每个文件的块列表和块所在的DataNode等.

2.DataNode(dn) : 在本地文件系统存储文件块数据,以及块数据的校验和.

3.Secondary NameNode(2nn):用来监控HDFS状态的辅助后台程序,每隔一段时间获取HDFS元数据的快照.

YARN 架构概述

四大组件 | RM / NM / AM / Container

MapReduce 架构概述

两大阶段 | Map / Reduce

MapReduce将计算过程分为两个阶段:Map 和 Reduce

Map阶段并行处理输入数据 | Reduce阶段对Map结果进行汇总

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大数据技术生态体系

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4. Hadoop 运行环境搭建(开发重点)

虚拟机环境准备

1.克隆虚拟机

enter image description here

2.修改克隆虚拟机的静态IP

使用root用户登录
vim /etc/udev/rules.d/70-persistent-net.rules

源代码

1.# This file was automatically generated by the /lib/udev/write_net_rules
2.# program, run by the persistent-net-generator.rules rules file.
3.#
4.# You can modify it, as long as you keep each rule on a single
5.# line, and change only the value of the NAME= key.
6.
7.# PCI device 0x8086:0x100f (e1000)
8.SUBSYSTEM=="net", ACTION=="add", DRIVERS=="?*", ATTR{address}=="00:0c:29:a3:d8:a7", ATTR{type}=="1", KERNEL=="eth*", NAME="eth0"
9.
10.# PCI device 0x8086:0x100f (e1000)
11.SUBSYSTEM=="net", ACTION=="add", DRIVERS=="?*", ATTR{address}=="00:0c:29:67:b3:77", ATTR{type}=="1", KERNEL=="eth*", NAME="eth1"

将NAME=”eth1”更改为NAME=”eth0”,并复制00:0c:29:67:b3:77地址

1.# PCI device 0x8086:0x100f (e1000)
2.SUBSYSTEM=="net", ACTION=="add", DRIVERS=="?*", ATTR{address}=="00:0c:29:67:b3:77", ATTR{type}=="1", KERNEL=="eth*", NAME="eth0"

更改完毕,:wq保存退出

修改网络配置
粘贴上一步地址,修改HWADDR属性
vim /etc/sysconfig/network-scripts/ifcfg-eth0

1.DEVICE=eth0
2.HWADDR=00:0c:29:67:b3:77
3.TYPE=Ethernet
4.UUID=b75136b3-4a81-41b5-9ebd-bfc1831d0df7
5.ONBOOT=yes
6.NM_CONTROLLED=yes
7.BOOTPROTO=static
8.
9.IPADDR=192.168.177.131
10.GATEWAY=192.168.177.2
11.DNS1=192.168.177.2

更改完毕,:wq保存退出

3.修改主机名

vim /etc/sysconfig/network

1.NETWORKING=yes
2.HOSTNAME=corehub-004

更改完毕,:wq保存退出

4.关闭防火墙

暂时性关闭防火墙:service iptables stop

5.创建用户

useradd username

6.配置用户具有root权限

vim /etc/sudoers

enter image description here
更改完毕,:wq!保存退出

7.在/opt目录下创建文件夹

1.创建software,module文件夹
software 用于日后存储的程序安装包
module 用于日后存储解析后的程序jar包

1.sudo mkdir software
2.sudo mkdir module

1.[geek-developer@corehub-001 ~]$ cd /opt/
2.[geek-developer@corehub-001 opt]$ ll
3.total 8
4.drwxr-xr-x. 6 root root 4096 Jan 17 23:35 devtool
5.drwxr-xr-x. 2 root root 4096 Oct  4  2017 rh
6.[geek-developer@corehub-001 opt]$ sudo mkdir software
7.[sudo] password for geek-developer: 
8.[geek-developer@corehub-001 opt]$ sudo mkdir module
9.[geek-developer@corehub-001 opt]$ ll
10.total 16
11.drwxr-xr-x. 6 root root 4096 Jan 17 23:35 devtool
12.drwxr-xr-x. 2 root root 4096 Jan 24 20:12 module
13.drwxr-xr-x. 2 root root 4096 Oct  4  2017 rh
14.drwxr-xr-x. 2 root root 4096 Jan 24 20:11 software
15.[geek-developer@corehub-001 opt]$

2.修改software,module文件夹的所有者

1.[geek-developer@corehub-001 opt]# chown geek-developer:geek-developer software/ module/
2.[geek-developer@corehub-001 opt]# ll
3.total 16
4.drwxr-xr-x. 6 root           root           4096 Jan 17 23:35 devtool
5.drwxr-xr-x. 2 geek-developer geek-developer 4096 Jan 24 20:12 module
6.drwxr-xr-x. 2 root           root           4096 Oct  4  2017 rh
7.drwxr-xr-x. 2 geek-developer geek-developer 4096 Jan 24 20:11 software
8.[geek-developer@corehub-001 opt]#

安装 Hadoop

在安装Hadoop前提是需要先安装JAVA并配置环境变量即可

Apache Hadoop官方地址 : https://archive.apache.org/dist/hadoop/common/hadoop-2.7.2/

通过远程工具,将hadoop-2.7.2.tar.gz传输到/op/tsoftware/目录下

将hadoop-2.7.2.tar.gz解压/opt/module/目录下

1.tar -zxvf hadoop-2.7.2.tar.gz -C /opt/module

将解压完成hadoop-2.7.2重命名为hadoop

1.[root@corehub-001 software]# cd ..
2.[root@corehub-001 opt]# cd module/
3.[root@corehub-001 module]# ll
4.total 4
5.drwxr-xr-x. 9 10011 10011 4096 Jan 26  2016 hadoop-2.7.2
6.[root@corehub-001 module]# mv hadoop-2.7.2 hadoop
7.[root@corehub-001 module]# ll
8.total 4
9.drwxr-xr-x. 9 10011 10011 4096 Jan 26  2016 hadoop
10.[root@corehub-001 module]#

配置hadoop环境变量

1.[root@corehub-001 module]# cd hadoop/
2.[root@corehub-001 hadoop]# pwd
3./opt/module/hadoop
4.[root@corehub-001 hadoop]#

1.[root@corehub-001 geek-developer]# vim /etc/profile

1.##HADOOP_HOME
2.export HADOOP_HOME=/opt/module/hadoop
3.export PATH=$PATH:$HADOOP_HOME/bin
4.export PATH=$PATH:$HADOOP_HOME/sbin

完成环境变量,:wq保存退出
source /etc/profile 更新配置文件指令

1.[root@corehub-001 geek-developer]# source /etc/profile
2.[root@corehub-001 geek-developer]# hadoop
3.Usage: hadoop [--config confdir] [COMMAND | CLASSNAME]
4.  CLASSNAME            run the class named CLASSNAME
5. or
6.  where COMMAND is one of:
7.  fs                   run a generic filesystem user client
8.  version              print the version
9.  jar <jar>            run a jar file
10.                       note: please use "yarn jar" to launch
11.                             YARN applications, not this command.
12.  checknative [-a|-h]  check native hadoop and compression libraries availability
13.  distcp <srcurl> <desturl> copy file or directories recursively
14.  archive -archiveName NAME -p <parent path> <src>* <dest> create a hadoop archive
15.  classpath            prints the class path needed to get the
16.  credential           interact with credential providers
17.                       Hadoop jar and the required libraries
18.  daemonlog            get/set the log level for each daemon
19.  trace                view and modify Hadoop tracing settings
20.
21.Most commands print help when invoked w/o parameters.
22.[root@corehub-001 geek-developer]#

Hadoop 目录结构

bin目录 : Hadoop服务脚本.

etc目录 : Hadoop的配置文件目录,存放Haoop配置文件.

lib目录 : 存放Hadoop本地库,(对数据进行压缩解压功能).

sbin目录 : 存放启动或停止Hadoop相关服务脚本.

share目录 : 存放Hadoop依赖jar包,文档,官方案例.

5. Hadoop 运行模式

Hadoop运行模式包括 : 本地运行 / 伪分布式运行 / 完全分布式运行

🎉🎉 本地运行模式 🎉🎉

👨‍💻👨‍💻 Grep 官方案例 👨‍💻👨‍💻

官方案例地址 : Standalone Operation

By default, Hadoop is configured to run in a non-distributed mode, as a single Java process. This is useful for debugging.

The following example copies the unpacked conf directory to use as input and then finds and displays every match of the given regular expression. Output is written to the given output directory.
1.$ mkdir input
2.$ cp etc/hadoop/*.xml input
3.$ bin/hadoop jar s

1.快速开始,在hadoop目录下创建input文件夹

1.[geek-developer@corehub-001 ~]$ cd /opt/module/hadoop/
2.[geek-developer@corehub-001 hadoop]$ ll
3.total 52
4.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 bin
5.drwxr-xr-x. 3 10011 10011  4096 Jan 26  2016 etc
6.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 include
7.drwxr-xr-x. 3 10011 10011  4096 Jan 26  2016 lib
8.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 libexec
9.-rw-r--r--. 1 10011 10011 15429 Jan 26  2016 LICENSE.txt
10.-rw-r--r--. 1 10011 10011   101 Jan 26  2016 NOTICE.txt
11.-rw-r--r--. 1 10011 10011  1366 Jan 26  2016 README.txt
12.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 sbin
13.drwxr-xr-x. 4 10011 10011  4096 Jan 26  2016 share
14.[geek-developer@corehub-001 hadoop]$ sudo mkdir input
15.[geek-developer@corehub-001 hadoop]$ ll
16.total 56
17.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 bin
18.drwxr-xr-x. 3 10011 10011  4096 Jan 26  2016 etc
19.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 include
20.drwxr-xr-x. 2 root  root   4096 Jan 24 22:23 input
21.drwxr-xr-x. 3 10011 10011  4096 Jan 26  2016 lib
22.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 libexec
23.-rw-r--r--. 1 10011 10011 15429 Jan 26  2016 LICENSE.txt
24.-rw-r--r--. 1 10011 10011   101 Jan 26  2016 NOTICE.txt
25.-rw-r--r--. 1 10011 10011  1366 Jan 26  2016 README.txt
26.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 sbin
27.drwxr-xr-x. 4 10011 10011  4096 Jan 26  2016 share
28.[geek-developer@corehub-001 hadoop]$

2.在hadoop目录中,将etc文件夹内以.xml为后缀的配置文件拷贝到input文件夹里

1.[geek-developer@corehub-001 hadoop]$ sudo cp etc/hadoop/*.xml input/
2.[geek-developer@corehub-001 hadoop]$ ls input/
3.capacity-scheduler.xml  hadoop-policy.xml  httpfs-site.xml  kms-site.xml
4.core-site.xml           hdfs-site.xml      kms-acls.xml     yarn-site.xml
5.[geek-developer@corehub-001 hadoop]$

3.执行share目录下的hadoop-mapreduce-examples-2.7.2.jar包,并指定输入和输出路径,以符合正则表达式并统计个数

dfs[a-z.]+ 以dfs开头,以a到z任意字符以.过滤掉 - - 字符

1.[root@corehub-001 geek-developer]# cd /opt/module/hadoop/
2.##########执行share目录下的hadoop-mapreduce-examples-2.7.2.jar包,并指定输入和输出路径#############
3.[root@corehub-001 hadoop]# hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.2.jar grep input/ output 'dfs[a-z.]+'
4.19/01/24 22:43:48 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
5.19/01/24 22:43:48 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
6.19/01/24 22:43:48 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
7.19/01/24 22:43:50 INFO output.FileOutputCommitter: Saved output of task 'attempt_local1034400674_0001_r_000000_0' to file:/opt/module/hadoop/grep-temp-1632689888/_temporary/0/task_local1034400674_0001_r_000000
8.19/01/24 22:43:50 INFO mapred.LocalJobRunner: reduce > reduce
9.19/01/24 22:43:50 INFO mapred.Task: Task 'attempt_local1034400674_0001_r_000000_0' done.
10.19/01/24 22:43:50 INFO mapred.LocalJobRunner: Finishing task: attempt_local1034400674_0001_r_000000_0
11.19/01/24 22:43:50 INFO mapred.LocalJobRunner: reduce task executor complete.
12.19/01/24 22:43:50 INFO mapreduce.Job: Job job_local1034400674_0001 running in uber mode : false
13.19/01/24 22:43:50 INFO mapreduce.Job:  map 100% reduce 100%
14.19/01/24 22:43:50 INFO mapreduce.Job: Job job_local1034400674_0001 completed successfully
15.19/01/24 22:43:50 INFO mapreduce.Job: Counters: 30
16.    File System Counters
17.        FILE: Number of bytes read=2691317
18.        FILE: Number of bytes written=5002436
19.        FILE: Number of read operations=0
20.        FILE: Number of large read operations=0
21.        FILE: Number of write operations=0
22.    Map-Reduce Framework
23.        Map input records=745
24.        Map output records=1
25.        Map output bytes=17
26.        Map output materialized bytes=67
27.        Input split bytes=877
28.        Combine input records=1
29.        Combine output records=1
30.        Reduce input groups=1
31.        Reduce shuffle bytes=67
32.        Reduce input records=1
33.        Reduce output records=1
34.        Spilled Records=2
35.        Shuffled Maps =8
36.        Failed Shuffles=0
37.        Merged Map outputs=8
38.        GC time elapsed (ms)=147
39.        Total committed heap usage (bytes)=2574778368
40.    Shuffle Errors
41.        BAD_ID=0
42.        CONNECTION=0
43.        IO_ERROR=0
44.        WRONG_LENGTH=0
45.        WRONG_MAP=0
46.        WRONG_REDUCE=0
47.    File Input Format Counters 
48.        Bytes Read=26007
49.    File Output Format Counters 
50.        Bytes Written=123
51.[root@corehub-001 hadoop]# ll
52.total 60
53.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 bin
54.drwxr-xr-x. 3 10011 10011  4096 Jan 26  2016 etc
55.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 include
56.drwxr-xr-x. 2 root  root   4096 Jan 24 22:28 input
57.drwxr-xr-x. 3 10011 10011  4096 Jan 26  2016 lib
58.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 libexec
59.-rw-r--r--. 1 10011 10011 15429 Jan 26  2016 LICENSE.txt
60.-rw-r--r--. 1 10011 10011   101 Jan 26  2016 NOTICE.txt
61.drwxr-xr-x. 2 root  root   4096 Jan 24 22:43 output
62.-rw-r--r--. 1 10011 10011  1366 Jan 26  2016 README.txt
63.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 sbin
64.drwxr-xr-x. 4 10011 10011  4096 Jan 26  2016 share
65.[root@corehub-001 hadoop]# ll output/
66.total 4
67.-rw-r--r--. 1 root root 11 Jan 24 22:43 part-r-00000
68.-rw-r--r--. 1 root root  0 Jan 24 22:43 _SUCCESS
69.############cd 进入output目录下############
70.[root@corehub-001 hadoop]# cd output/
71.############最后查看符合正则表达式并统计个数############
72.[root@corehub-001 output]# cat part-r-00000 
73.1   dfsadmin
74.[root@corehub-001 output]#

👨‍💻👨‍💻 WordCount 官方案例 👨‍💻👨‍💻

1.在hadoop目录下,创建一个wcinput文件夹

1.[root@corehub-001 hadoop]# mkdir wcinput
2.[root@corehub-001 hadoop]# ll
3.total 64
4.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 bin
5.drwxr-xr-x. 3 10011 10011  4096 Jan 26  2016 etc
6.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 include
7.drwxr-xr-x. 2 root  root   4096 Jan 24 22:28 input
8.drwxr-xr-x. 3 10011 10011  4096 Jan 26  2016 lib
9.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 libexec
10.-rw-r--r--. 1 10011 10011 15429 Jan 26  2016 LICENSE.txt
11.-rw-r--r--. 1 10011 10011   101 Jan 26  2016 NOTICE.txt
12.drwxr-xr-x. 2 root  root   4096 Jan 24 22:43 output
13.-rw-r--r--. 1 10011 10011  1366 Jan 26  2016 README.txt
14.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 sbin
15.drwxr-xr-x. 4 10011 10011  4096 Jan 26  2016 share
16.drwxr-xr-x. 2 root  root   4096 Jan 24 23:07 wcinput
17.[root@corehub-001 hadoop]#

2.在wcinput文件下创建一个wc.input文件

1.[root@corehub-001 hadoop]# cd wcinput/
2.[root@corehub-001 wcinput]# touch wc.input
3.[root@corehub-001 wcinput]# ll
4.total 0
5.-rw-r--r--. 1 root root 0 Jan 24 23:08 wc.input
6.[root@corehub-001 wcinput]#

3.编辑wc.input文件并在文件中输入内容,输入完毕按esc,输入:wq保存退出

1.[root@corehub-001 wcinput]# vim wc.input

1.hello-world
2.hello-world
3.java
4.python
5.php
6.golang
7.hadoop yarn
8.hadoop mapreduce
9.hive
10.spark
11.java
12.springcloud
13.springboot
14.geek
15.geekpark
16.geekparkhub
17.geekparkhub
18.geek-developer
19.jeep-711
20.jeep-711
21.github
22.~                                           
23.~

4.返回hadoop目录

1.[root@corehub-001 wcinput]# cd ..
2.[root@corehub-001 hadoop]#

5.执行程序

1.[root@corehub-001 hadoop]# hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.2.jar wordcount wcinput wcoutput
2.19/01/24 23:20:50 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
3.19/01/24 23:20:50 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
4.19/01/24 23:20:50 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
5.19/01/24 23:20:50 INFO input.FileInputFormat: Total input paths to process : 1
6.19/01/24 23:20:50 INFO mapreduce.JobSubmitter: number of splits:1
7.19/01/24 23:20:51 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_local450202257_0001
8.19/01/24 23:20:51 INFO mapreduce.Job: The url to track the job: http://localhost:8080/
9.19/01/24 23:20:51 INFO mapreduce.Job: Running job: job_local450202257_0001
10.19/01/24 23:20:51 INFO output.FileOutputCommitter: Saved output of task 'attempt_local450202257_0001_r_000000_0' to file:/opt/module/hadoop/wcoutput/_temporary/0/task_local450202257_0001_r_000000
11.19/01/24 23:20:51 INFO mapred.LocalJobRunner: reduce > reduce
12.19/01/24 23:20:51 INFO mapred.Task: Task 'attempt_local450202257_0001_r_000000_0' done.
13.19/01/24 23:20:51 INFO mapred.LocalJobRunner: Finishing task: attempt_local450202257_0001_r_000000_0
14.19/01/24 23:20:51 INFO mapred.LocalJobRunner: reduce task executor complete.
15.19/01/24 23:20:52 INFO mapreduce.Job: Job job_local450202257_0001 running in uber mode : false
16.19/01/24 23:20:52 INFO mapreduce.Job:  map 100% reduce 100%
17.19/01/24 23:20:52 INFO mapreduce.Job: Job job_local450202257_0001 completed successfully
18.19/01/24 23:20:52 INFO mapreduce.Job: Counters: 30
19.    File System Counters
20.        FILE: Number of bytes read=547482
21.        FILE: Number of bytes written=1105096
22.        FILE: Number of read operations=0
23.        FILE: Number of large read operations=0
24.        FILE: Number of write operations=0
25.    Map-Reduce Framework
26.        Map input records=4
27.        Map output records=6
28.        Map output bytes=77
29.        Map output materialized bytes=77
30.        Input split bytes=105
31.        Combine input records=6
32.        Combine output records=5
33.        Reduce input groups=5
34.        Reduce shuffle bytes=77
35.        Reduce input records=5
36.        Reduce output records=5
37.        Spilled Records=10
38.        Shuffled Maps =1
39.        Failed Shuffles=0
40.        Merged Map outputs=1
41.        GC time elapsed (ms)=0
42.        Total committed heap usage (bytes)=397410304
43.    Shuffle Errors
44.        BAD_ID=0
45.        CONNECTION=0
46.        IO_ERROR=0
47.        WRONG_LENGTH=0
48.        WRONG_MAP=0
49.        WRONG_REDUCE=0
50.    File Input Format Counters 
51.        Bytes Read=53
52.    File Output Format Counters 
53.        Bytes Written=63
54.[root@corehub-001 hadoop]#

6.查看统计单词的个数结果 | 😍😍 深深感受到大数据的魅力所在 😍😍

1.[root@corehub-001 hadoop]# ll
2.total 68
3.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 bin
4.drwxr-xr-x. 3 10011 10011  4096 Jan 26  2016 etc
5.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 include
6.drwxr-xr-x. 2 root  root   4096 Jan 24 22:28 input
7.drwxr-xr-x. 3 10011 10011  4096 Jan 26  2016 lib
8.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 libexec
9.-rw-r--r--. 1 10011 10011 15429 Jan 26  2016 LICENSE.txt
10.-rw-r--r--. 1 10011 10011   101 Jan 26  2016 NOTICE.txt
11.drwxr-xr-x. 2 root  root   4096 Jan 24 22:43 output
12.-rw-r--r--. 1 10011 10011  1366 Jan 26  2016 README.txt
13.drwxr-xr-x. 2 10011 10011  4096 Jan 26  2016 sbin
14.drwxr-xr-x. 4 10011 10011  4096 Jan 26  2016 share
15.drwxr-xr-x. 2 root  root   4096 Jan 24 23:33 wcinput
16.drwxr-xr-x. 2 root  root   4096 Jan 24 23:34 wcoutput
17.[root@corehub-001 hadoop]# ll wcoutput/
18.total 4
19.-rw-r--r--. 1 root root 184 Jan 24 23:34 part-r-00000
20.-rw-r--r--. 1 root root   0 Jan 24 23:34 _SUCCESS
21.[root@corehub-001 hadoop]# cd wcoutput/
22.[root@corehub-001 wcoutput]# cat part-r-00000 
23.geek    1
24.geek-developer  1
25.geekpark    1
26.geekparkhub 2
27.github  1
28.golang  1
29.hadoop  2
30.hello-world 2
31.hive    1
32.java    2
33.jeep-711    2
34.mapreduce   1
35.php 1
36.python  1
37.spark   1
38.springboot  1
39.springcloud 1
40.yarn    1
41.[root@corehub-001 wcoutput]#

🎉🎉 伪分布式运行模式 🎉🎉

启动HDFS并运行MapReduce程序

分析 : 配置集群,启动测试集群增删查,执行WordCount案例

配置集群

配置 hadoop-env.sh,获取jdk安装路径

获取并复制JAVA_HOME路径

1.[root@corehub-001 hadoop]# echo $JAVA_HOME
2./opt/jdk1.8.0_162

配置hadoop-env.sh

1.[root@corehub-001 hadoop]# vim etc/hadoop/hadoop-env.sh

1.# The only required environment variable is JAVA_HOME.  All others are
2.# optional.  When running a distributed configuration it is best to
3.# set JAVA_HOME in this file, so that it is correctly defined on
4.# remote nodes.
5.
6.# The java implementation to use.
7.export JAVA_HOME=/opt/jdk1.8.0_162

配置 `core-site.xml`

core-site.xml 官方文档说明 : core-default.xml

1.[root@corehub-001 hadoop]# vim etc/hadoop/core-site.xml

1.<?xml version="1.0" encoding="UTF-8"?>
2.<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
3.<!--
4.  Licensed under the Apache License, Version 2.0 (the "License");
5.  you may not use this file except in compliance with the License.
6.  You may obtain a copy of the License at
7.
8.    http://www.apache.org/licenses/LICENSE-2.0
9.
10.  Unless required by applicable law or agreed to in writing, software
11.  distributed under the License is distributed on an "AS IS" BASIS,
12.  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13.  See the License for the specific language governing permissions and
14.  limitations under the License. See accompanying LICENSE file.
15.-->
16.<!-- Put site-specific property overrides in this file. -->
17.
18.<configuration>
19.<!-- 指定HDFS中的NameNode地址 -->
20.  <property>
21.    <name>fs.defaultFS</name>
22.    <value>hdfs://corehub-001:9000</value>
23.  </property>
24.<!-- 指定Hadoop运行时产生文件的存储目录 -->
25.   <property>
26.     <name>hadoop.tmp.dir</name>
27.     <value>/opt/module/hadoop/data/tmp</value>
28.   </property>
29.</configuration>

输入完毕按esc,输入:wq保存退出

配置 `hdfs.site.xml`

hdfs.site.xml 官方文档说明 : hdfs-default.xml

1.[root@corehub-001 hadoop]# vim etc/hadoop/hdfs-site.xml

1.<?xml version="1.0" encoding="UTF-8"?>
2.<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
3.<!--
4.  Licensed under the Apache License, Version 2.0 (the "License");
5.  you may not use this file except in compliance with the License.
6.  You may obtain a copy of the License at
7.
8.    http://www.apache.org/licenses/LICENSE-2.0
9.
10.  Unless required by applicable law or agreed to in writing, software
11.  distributed under the License is distributed on an "AS IS" BASIS,
12.  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13.  See the License for the specific language governing permissions and
14.  limitations under the License. See accompanying LICENSE file.
15.-->
16.
17.<!-- Put site-specific property overrides in this file. -->
18.
19.<configuration>
20.<!-- 指定HDFS副本数量 -->
21.  <property>
22.   <name>dfs.replication</name>
23.     <value>1</value>
24.  </property>
25.</configuration>

输入完毕按esc,输入:wq保存退出

启动集群

格式化 NameNode

(第一次初始化启动需要格式化,只需在首次启动前格式化)

1.[root@corehub-001 hadoop]# bin/hdfs namenode -format
2.19/01/25 12:59:38 INFO namenode.NameNode: STARTUP_MSG: 
3./************************************************************
4.STARTUP_MSG: Starting NameNode
5.STARTUP_MSG:   host = corehub-001/192.168.152.130
6.STARTUP_MSG:   args = [-format]
7.STARTUP_MSG:   version = 2.7.2
8.STARTUP_MSG:   classpath = /opt/module/hadoop/etc/hadoop:/opt/module/hadoop/share/hadoop/common/lib/jsp-api-2.1.jar:/opt/module/hadoop/share/hadoop/common/lib/servlet-api-2.5.jar:/opt/module/hadoop/share/hadoop/common/lib/jsch-
9.STARTUP_MSG:   build = https://git-wip-us.apache.org/repos/asf/hadoop.git -r b165c4fe8a74265c792ce23f546c64604acf0e41; compiled by 'jenkins' on 2016-01-26T00:08Z
10.STARTUP_MSG:   java = 1.8.0_162
11.************************************************************/
12.19/01/25 12:59:38 INFO namenode.NameNode: registered UNIX signal handlers for [TERM, HUP, INT]
13.19/01/25 12:59:38 INFO namenode.NameNode: createNameNode [-format]
14.19/01/25 12:59:38 INFO blockmanagement.DatanodeManager: dfs.block.invalidate.limit=1000
15.19/01/25 12:59:38 INFO blockmanagement.DatanodeManager: dfs.namenode.datanode.registration.ip-hostname-check=true
16.19/01/25 12:59:38 INFO blockmanagement.BlockManager: dfs.namenode.startup.delay.block.deletion.sec is set to 000:00:00:00.000
17.19/01/25 12:59:38 INFO blockmanagement.BlockManager: The block deletion will start around 2019 Jan 25 12:59:38
18.19/01/25 12:59:39 INFO util.GSet: Computing capacity for map NameNodeRetryCache
19.19/01/25 12:59:39 INFO util.GSet: VM type       = 64-bit
20.19/01/25 12:59:39 INFO util.GSet: 0.029999999329447746% max memory 889 MB = 273.1 KB
21.19/01/25 12:59:39 INFO util.GSet: capacity      = 2^15 = 32768 entries
22.19/01/25 12:59:39 INFO namenode.FSImage: Allocated new BlockPoolId: BP-169105537-192.168.152.130-1548449979185
23.19/01/25 12:59:39 INFO common.Storage: Storage directory /opt/module/hadoop/data/tmp/dfs/name has been successfully formatted.
24.19/01/25 12:59:39 INFO namenode.NNStorageRetentionManager: Going to retain 1 images with txid >= 0
25.19/01/25 12:59:39 INFO util.ExitUtil: Exiting with status 0
26.19/01/25 12:59:39 INFO namenode.NameNode: SHUTDOWN_MSG: 
27./************************************************************
28.SHUTDOWN_MSG: Shutting down NameNode at corehub-001/192.168.152.130
29.************************************************************/
30.[root@corehub-001 hadoop]# ll
31.total 72
32.drwxr-xr-x. 2 10011 10011  4096 Jan 25  2016 bin
33.drwxr-xr-x. 3 root  root   4096 Jan 25 12:59 data
34.drwxr-xr-x. 3 10011 10011  4096 Jan 25  2016 etc
35.drwxr-xr-x. 2 10011 10011  4096 Jan 25  2016 include
36.drwxr-xr-x. 2 root  root   4096 Jan 25 09:44 input
37.drwxr-xr-x. 3 10011 10011  4096 Jan 25  2016 lib
38.drwxr-xr-x. 2 10011 10011  4096 Jan 25  2016 libexec
39.-rw-r--r--. 1 10011 10011 15429 Jan 25  2016 LICENSE.txt
40.-rw-r--r--. 1 10011 10011   101 Jan 25  2016 NOTICE.txt
41.drwxr-xr-x. 2 root  root   4096 Jan 25 09:45 output
42.-rw-r--r--. 1 10011 10011  1366 Jan 25  2016 README.txt
43.drwxr-xr-x. 2 10011 10011  4096 Jan 25  2016 sbin
44.drwxr-xr-x. 4 10011 10011  4096 Jan 25  2016 share
45.drwxr-xr-x. 2 root  root   4096 Jan 25 09:47 wcinput
46.drwxr-xr-x. 2 root  root   4096 Jan 25 09:48 wcoutput
47.[root@corehub-001 hadoop]#

启动 NameNode

1.[root@corehub-001 hadoop]# sbin/hadoop-daemon.sh start namenode
2.starting namenode, logging to /opt/module/hadoop/logs/hadoop-root-namenode-corehub-001.out
3.[root@corehub-001 hadoop]# jps
4.3153 Jps
5.3022 NameNode

启动 DataNode

1.[root@corehub-001 hadoop]# sbin/hadoop-daemon.sh start datanode
2.starting datanode, logging to /opt/module/hadoop/logs/hadoop-root-datanode-corehub-001.out
3.[root@corehub-001 hadoop]# jps
4.3696 DataNode
5.3858 Jps
6.3022 NameNode
7.[root@corehub-001 hadoop]#

查看是否启动成功

可通过hadoop提供website图形化界面查看启动结果

通过 LinuxHostName:50070端口号形式访问或通过 Linux IPaddr:50070端口号访问

以hdfs指令创建多级目录

类似于Linux目录树结构一致,可见熟练掌握LInux命令尤为重要

enter image description here

1.[root@corehub-001 hadoop]# bin/hdfs dfs -mkdir -p /user/geekparkhub/input
2.19/01/25 14:41:14 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

1.[root@corehub-001 hadoop]# bin/hdfs dfs -ls -R /
2.19/01/25 14:44:20 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
3.drwxr-xr-x   - root supergroup          0 2019-01-25 14:41 /user
4.drwxr-xr-x   - root supergroup          0 2019-01-25 14:41 /user/geekparkhub
5.drwxr-xr-x   - root supergroup          0 2019-01-25 14:41 /user/geekparkhub/input
6.[root@corehub-001 hadoop]#

在hadoop目录下,将wcinput目录及元数据文件上传到/user/geekparkhub/input目录中

enter image description here

1.[root@corehub-001 hadoop]# bin/hdfs dfs -put wcinput/wc.input /user/geekparkhub/input
2.19/01/25 15:02:56 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
3.[root@corehub-001 hadoop]#

执行并读取HDFS程序

1.[root@corehub-001 hadoop]# hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.2.jar wordcount /user/geekparkhub/input /user/geekparkhub/output
2.19/01/25 15:16:04 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
3.19/01/25 15:16:05 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
4.19/01/25 15:16:05 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
5.19/01/25 15:16:05 INFO input.FileInputFormat: Total input paths to process : 1
6.19/01/25 15:16:08 INFO mapreduce.Job:  map 100% reduce 100%
7.19/01/25 15:16:08 INFO mapreduce.Job: Job job_local169102714_0001 completed successfully
8.19/01/25 15:16:08 INFO mapreduce.Job: Counters: 35
9.        File System Counters
10.        Shuffle Errors
11.                BAD_ID=0
12.                CONNECTION=0
13.                IO_ERROR=0
14.                WRONG_LENGTH=0
15.                WRONG_MAP=0
16.                WRONG_REDUCE=0
17.        File Input Format Counters 
18.                Bytes Read=471
19.        File Output Format Counters 
20.                Bytes Written=503
21.[root@corehub-001 hadoop]#

启动YARN上运行MapReduce程序

分析 : 配置集群在Yarn上运行MR,启动测试集群增删查,在Yarn上执行Wordcount案例

配置集群

配置yarn-env.sh

vim etc/hadoop/yarn-env.sh

1.# Licensed to the Apache Software Foundation (ASF) under one or more
2.# contributor license agreements.  See the NOTICE file distributed with
3.# this work for additional information regarding copyright ownership.
4.# The ASF licenses this file to You under the Apache License, Version 2.0
5.# (the "License"); you may not use this file except in compliance with
6.# the License.  You may obtain a copy of the License at
7.#
8.#     http://www.apache.org/licenses/LICENSE-2.0
9.#
10.# Unless required by applicable law or agreed to in writing, software
11.# distributed under the License is distributed on an "AS IS" BASIS,
12.# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13.# See the License for the specific language governing permissions and
14.# limitations under the License.
15.
16.# User for YARN daemons
17.export HADOOP_YARN_USER=${HADOOP_YARN_USER:-yarn}
18.
19.# resolve links - $0 may be a softlink
20.export YARN_CONF_DIR="${YARN_CONF_DIR:-$HADOOP_YARN_HOME/conf}"
21.
22.# some Java parameters
23.export JAVA_HOME=/opt/jdk1.8.0_162
24.if [ "$JAVA_HOME" != "" ]; then
25.  #echo "run java in $JAVA_HOME"
26.  JAVA_HOME=$JAVA_HOME
27.fi
28.
29.if [ "$JAVA_HOME" = "" ]; then
30.  echo "Error: JAVA_HOME is not set."
31.  exit 1
32.fi
33.
34.JAVA=$JAVA_HOME/bin/java
35.JAVA_HEAP_MAX=-Xmx1000m

配置yarn-site.xml

yarn-site.xml 官方文档说明 : yarn-default.xml

1.[root@corehub-001 hadoop]# vim etc/hadoop/yarn-site.xml

1.<?xml version="1.0"?>
2.<!--
3.  Licensed under the Apache License, Version 2.0 (the "License");
4.  you may not use this file except in compliance with the License.
5.  You may obtain a copy of the License at
6.
7.    http://www.apache.org/licenses/LICENSE-2.0
8.
9.  Unless required by applicable law or agreed to in writing, software
10.  distributed under the License is distributed on an "AS IS" BASIS,
11.  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12.  See the License for the specific language governing permissions and
13.  limitations under the License. See accompanying LICENSE file.
14.-->
15.<configuration>
16.<!-- Site specific YARN configuration properties -->
17.<!-- Reducer获取数据方式 -->
18.    <property>
19.      <name>yarn.nodemanager.aux-services</name>
20.      <value>mapreduce_shuffle</value>
21.    </property>
22.<!-- 指定Yarn的ResourceManager地址-->
23.    <property>
24.      <name>yarn.resourcemanager.hostname</name>
25.      <value>corehub-001</value>
26.    </property>
27.</configuration>

配置mapred-env.sh

vim etc/hadoop/mapred-env.sh

1.[root@corehub-001 hadoop]# vim etc/hadoop/mapred-env.sh
2.# Licensed to the Apache Software Foundation (ASF) under one or more
3.# contributor license agreements.  See the NOTICE file distributed with
4.# this work for additional information regarding copyright ownership.
5.# The ASF licenses this file to You under the Apache License, Version 2.0
6.# (the "License"); you may not use this file except in compliance with
7.# the License.  You may obtain a copy of the License at
8.#
9.#     http://www.apache.org/licenses/LICENSE-2.0
10.#
11.# Unless required by applicable law or agreed to in writing, software
12.# distributed under the License is distributed on an "AS IS" BASIS,
13.# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14.# See the License for the specific language governing permissions and
15.# limitations under the License.
16.
17.export JAVA_HOME=/opt/jdk1.8.0_162
18.
19.export HADOOP_JOB_HISTORYSERVER_HEAPSIZE=1000
20.
21.export HADOOP_MAPRED_ROOT_LOGGER=INFO,RFA
22.
23.#export HADOOP_JOB_HISTORYSERVER_OPTS=
24.#export HADOOP_MAPRED_LOG_DIR="" # Where log files are stored.  $HADOOP_MAPRED_HOME/logs by default.
25.#export HADOOP_JHS_LOGGER=INFO,RFA # Hadoop JobSummary logger.
26.#export HADOOP_MAPRED_PID_DIR= # The pid files are stored. /tmp by default.
27.#export HADOOP_MAPRED_IDENT_STRING= #A string representing this instance of hadoop. $USER by default
28.#export HADOOP_MAPRED_NICENESS= #The scheduling priority for daemons. Defaults to 0.

配置mapred-site.xml

mapred-site.xml.template 重命名为mapred-site.xml

1.[root@corehub-001 hadoop]# mv etc/hadoop/mapred-site.xml.template etc/hadoop/mapred-site.xml

1.[root@corehub-001 hadoop]# vim etc/hadoop/mapred-site.xml

1.<?xml version="1.0"?>
2.<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
3.<!--
4.  Licensed under the Apache License, Version 2.0 (the "License");
5.  you may not use this file except in compliance with the License.
6.  You may obtain a copy of the License at
7.    http://www.apache.org/licenses/LICENSE-2.0
8.  Unless required by applicable law or agreed to in writing, software
9.  distributed under the License is distributed on an "AS IS" BASIS,
10.  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11.  See the License for the specific language governing permissions and
12.  limitations under the License. See accompanying LICENSE file.
13.-->
14.<!-- Put site-specific property overrides in this file. -->
15.<configuration>
16.<!-- 指定MR运行在Yarn上 -->
17.    <property>
18.      <name>mapreduce.framework.name</name>
19.      <value>yarn</value>
20.    </property>
21.</configuration>

启动集群

启动前必须保证NameNode和DataNode已经启动

启动ResourceManager

1.[root@corehub-001 hadoop]# sbin/yarn-daemon.sh start resourcemanager
2.starting resourcemanager, logging to /opt/module/hadoop/logs/yarn-root-resourcemanager-corehub-001.out
3.[root@corehub-001 hadoop]# jps
4.39653 ResourceManager
5.9353 DataNode
6.9066 NameNode
7.40171 Jps
8.[root@corehub-001 hadoop]#

启动NodeManager

1.[root@corehub-001 hadoop]# sbin/yarn-daemon.sh start nodemanager
2.starting nodemanager, logging to /opt/module/hadoop/logs/yarn-root-nodemanager-corehub-001.out
3.[root@corehub-001 hadoop]# jps
4.40880 Jps
5.40769 NodeManager
6.39653 ResourceManager
7.9353 DataNode
8.9066 NameNode
9.[root@corehub-001 hadoop]#

执行wordcount程序并查看运行结果

可通过hadoop提供website图形化界面查看启动结果

通过 LinuxHostName:8088端口号形式访问或通过 Linux IPaddr:8088端口号访问

enter image description here

1.[root@corehub-001 hadoop]# hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.2.jar wordcount /user/geekparkhub/input /user/geekparkhub/output
2.^H19/01/27 19:37:36 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
3.19/01/27 19:37:37 INFO client.RMProxy: Connecting to ResourceManager at corehub-001/192.168.177.130:8032
4.19/01/27 19:37:38 INFO input.FileInputFormat: Total input paths to process : 1
5.19/01/27 19:37:38 INFO mapreduce.JobSubmitter: number of splits:1
6.19/01/27 19:37:38 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1548588180141_0001
7.19/01/27 19:37:39 INFO impl.YarnClientImpl: Submitted application application_1548588180141_0001
8.19/01/27 19:37:39 INFO mapreduce.Job: The url to track the job: http://corehub-001:8088/proxy/application_1548588180141_0001/
9.19/01/27 19:37:39 INFO mapreduce.Job: Running job: job_1548588180141_0001
10.19/01/27 19:37:53 INFO mapreduce.Job: Job job_1548588180141_0001 running in uber mode : false
11.19/01/27 19:37:53 INFO mapreduce.Job:  map 0% reduce 0%
12.19/01/27 19:38:01 INFO mapreduce.Job:  map 100% reduce 0%
13.19/01/27 19:38:08 INFO mapreduce.Job:  map 100% reduce 100%
14.19/01/27 19:38:08 INFO mapreduce.Job: Job job_1548588180141_0001 completed successfully
15.19/01/27 19:38:08 INFO mapreduce.Job: Counters: 49
16.        Job Counters 
17.                Launched map tasks=1
18.                Launched reduce tasks=1
19.                Data-local map tasks=1
20.                Total time spent by all maps in occupied slots (ms)=5298
21.                Total time spent by all reduces in occupied slots (ms)=4839
22.                Total time spent by all map tasks (ms)=5298
23.                Total time spent by all reduce tasks (ms)=4839
24.                Total vcore-milliseconds taken by all map tasks=5298
25.                Total vcore-milliseconds taken by all reduce tasks=4839
26.                Total megabyte-milliseconds taken by all map tasks=5425152
27.                Total megabyte-milliseconds taken by all reduce tasks=4955136
28.        Map-Reduce Framework
29.                Map input records=24
30.                Map output records=23
31.                Map output bytes=285
32.                Map output materialized bytes=262
33.                Input split bytes=120
34.                Combine input records=23
35.                Combine output records=18
36.                Reduce input groups=18
37.                Reduce shuffle bytes=262
38.                Reduce input records=18
39.                Reduce output records=18
40.                Spilled Records=36
41.                Shuffled Maps =1
42.                Failed Shuffles=0
43.                Merged Map outputs=1
44.                GC time elapsed (ms)=164
45.                CPU time spent (ms)=1830
46.                Physical memory (bytes) snapshot=416026624
47.                Virtual memory (bytes) snapshot=4163923968
48.                Total committed heap usage (bytes)=275775488
49.        Shuffle Errors
50.                BAD_ID=0
51.                CONNECTION=0
52.                IO_ERROR=0
53.                WRONG_LENGTH=0
54.                WRONG_MAP=0
55.                WRONG_REDUCE=0
56.        File Input Format Counters 
57.                Bytes Read=196
58.        File Output Format Counters 
59.                Bytes Written=184

配置历史服务器

为了查看程序历史运行情况,需要配置一下历史服务器

配置mapred-site.xml

1.[root@corehub-001 hadoop]# vim etc/hadoop/mapred-site.xml

1.<?xml version="1.0"?>
2.<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
3.<!--
4.  Licensed under the Apache License, Version 2.0 (the "License");
5.  you may not use this file except in compliance with the License.
6.  You may obtain a copy of the License at
7.
8.    http://www.apache.org/licenses/LICENSE-2.0
9.
10.  Unless required by applicable law or agreed to in writing, software
11.  distributed under the License is distributed on an "AS IS" BASIS,
12.  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13.  See the License for the specific language governing permissions and
14.  limitations under the License. See accompanying LICENSE file.
15.-->
16.
17.<!-- Put site-specific property overrides in this file. -->
18.
19.<configuration>
20.<!-- 指定MR运行在Yarn上 -->
21.    <property>
22.      <name>mapreduce.framework.name</name>
23.      <value>yarn</value>
24.    </property>
25.<!-- 历史服务器地址 -->
26.    <property>
27.       <name>mapreduce.jobhistory.address</name>
28.       <value>corehub-001:10020</value>
29.    </property>
30.<!-- 历史服务器WEB地址 -->
31.    <property>
32.       <name>mapreduce.jobhistory.webapp.address</name>
33.       <value>corehub-001:19888</value>
34.    </property>
35.</configuration>

启动历史服务器并查看查看历史服务器是否启动以及JobHistory运行状态

1.[root@corehub-001 hadoop]# sbin/mr-jobhistory-daemon.sh start historyserver
2.starting historyserver, logging to /opt/module/hadoop/logs/mapred-root-historyserver-corehub-001.out
3.[root@corehub-001 hadoop]# jps
4.40769 NodeManager
5.66818 JobHistoryServer
6.39653 ResourceManager
7.66948 Jps
8.9353 DataNode
9.9066 NameNode
10.[root@corehub-001 hadoop]#

enter image description here

配置日志聚集

日志聚集概念:应用运行完成以后,将程序运行日志信息上传到HDFS系统上.
日志聚集功能优势:可以方便查看程序运行详情,方便开发调试.
注意:开启日志聚集功能,需要重新启动NodeManager,ResourceManager,HistoryManager此三项服务.

配置文件说明

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🎉🎉 完全分布式运行模式 (开发重点) 🎉🎉

虚拟机准备

编写集群分发脚本xsync

集群配置

集群单点启动

SSH无密码配置

群起集群

集群启动/关闭方式总结

集群时间同步

6. Hadoop 编译源码

前期准备工作

jar包安装

编译源码

7. 常见错误(各种坑)及解决方案

8. 修仙之道技术架构迭代登峰造极之势

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License 开源协议

Apache License Version 2.0

大数据生态系统 修仙之道 Hadoop Blog

🐘 Hadoop Technology 修仙之道 炼精化气 🐘

1. 大数据 简介

2. 大数据 概论

大数据 概念

大数据 特点(4V)

1.Volume (大量)

2.Velocity (高速)

3.Variety (多样)

4.Value (低价值密度)

大数据 应用场景

大数据部门业务流程分析

大数据部门组织结构(重点)

3. 探讨Hadoop框架 大数据生态

Hadoop 简介

Hadoop 是什么

Hadoop 发展历史

Hadoop 三大发行版本

Apache Hadoop

Cloudera Hadoop

Hortonworks Hadoop

Hadoop 优势 (4高)

1.高可靠性

2.高扩展性

3.高效性

4.高容错性

Hadoop 组成(面试重点)

Hadoop1.x与Hadoop2.x 区别

HDFS 架构概述

YARN 架构概述

MapReduce 架构概述

大数据技术 生态体系

推荐 系统框架图

4. Hadoop 运行环境搭建(开发重点)

虚拟机环境 准备

1.克隆虚拟机

2.修改克隆虚拟机的静态IP

3.修改主机名

4.关闭防火墙

5.创建用户

6.配置用户具有root权限

7.在/opt目录下创建文件夹

安装 Hadoop

Hadoop 目录结构

5. Hadoop 运行模式

🎉🎉 本地运行模式 🎉🎉

👨‍💻👨‍💻 Grep 官方案例 👨‍💻👨‍💻

1.快速开始,在hadoop目录下 创建input文件夹

2.在hadoop目录中,将etc文件夹内 以.xml为后缀的配置文件拷贝到input文件夹里

3.执行share目录下的hadoop-mapreduce-examples-2.7.2.jar包,并指定输入和输出路径,以符合正则表达式并统计个数

👨‍💻👨‍💻 WordCount 官方案例 👨‍💻👨‍💻

1.在hadoop目录下,创建一个wcinput文件夹

2.在wcinput文件下创建一个wc.input文件

3.编辑wc.input文件并在文件中输入内容,输入完毕按esc,输入:wq保存退出

4.返回hadoop目录

5.执行程序

6.查看统计单词的个数结果 | 😍😍 深深感受到大数据的魅力所在 😍😍

🎉🎉 伪分布式 运行模式 🎉🎉

启动HDFS并运行MapReduce程序

配置集群

配置 hadoop-env.sh,获取jdk安装路径

配置 core-site.xml

配置 hdfs.site.xml

启动集群

格式化 NameNode

启动 NameNode

启动 DataNode

查看是否启动成功

以hdfs指令 创建多级目录

在hadoop目录下,将wcinput目录及元数据文件上传到/user/geekparkhub/input目录中

执行并读取HDFS程序

启动YARN上运行MapReduce程序

配置集群

配置yarn-env.sh

配置yarn-site.xml

配置mapred-env.sh

配置mapred-site.xml

启动集群

启动ResourceManager

启动NodeManager

大数据生态系统修仙之道 Hadoop Blog

🐘 Hadoop Technology 修仙之道炼精化气 🐘

1. 大数据简介

2. 大数据概论

大数据概念

大数据特点(4V)

大数据应用场景

3. 探讨Hadoop框架大数据生态

大数据技术生态体系

推荐系统框架图

虚拟机环境准备

1.快速开始,在hadoop目录下创建input文件夹

2.在hadoop目录中,将etc文件夹内以.xml为后缀的配置文件拷贝到input文件夹里

🎉🎉 伪分布式运行模式 🎉🎉

配置 `core-site.xml`

配置 `hdfs.site.xml`

以hdfs指令创建多级目录

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🎉🎉 完全分布式运行模式 (开发重点) 🎉🎉

8. 修仙之道技术架构迭代登峰造极之势

💌感谢您的阅读欢迎您的留言与建议💌

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